Dynamics of Multistable States during Ongoing and Evoked Cortical Activity

Abstract

Single-trial analyses of ensemble activity in alert animals demonstrate that cortical circuits dynamics evolve through temporal sequences of metastable states. Metastability has been studied for its potential role in sensory coding, memory, and decision-making. Yet, very little is known about the network mechanisms responsible for its genesis. It is often assumed that the onset of state sequences is triggered by an external stimulus. Here we show that state sequences can be observed also in the absence of overt sensory stimulation. Analysis of multielectrode recordings from the gustatory cortex of alert rats revealed ongoing sequences of states, where single neurons spontaneously attain several firing rates across different states. This single-neuron multistability represents a challenge to existing spiking network models, where typically each neuron is at most bistable. We present a recurrent spiking network model that accounts for both the spontaneous generation of state sequences and the multistability in single-neuron firing rates. Each state results from the activation of neural clusters with potentiated intracluster connections, with the firing rate in each cluster depending on the number of active clusters. Simulations show that the model's ensemble activity hops among the different states, reproducing the ongoing dynamics observed in the data. When probed with external stimuli, the model predicts the quenching of single-neuron multistability into bistability and the reduction of trial-by-trial variability. Both predictions were confirmed in the data. Together, these results provide a theoretical framework that captures both ongoing and evoked network dynamics in a single mechanistic model.

Itinerancy between attractor states in neural systems

“…In support of the hypothesis that spontaneous activity samples attractor states, multi-electrode data from awake ferrets shows that spontaneous activity in vivo approaches activity evoked by natural stimuli over the course of development [43], with statistics more compatible with wandering between multiple states than with fluctuations about a single state [44]. Recently, direct evidence from HMM demonstrates that spontaneous activity in rodents can exhibit rapid transitions between discrete states [45]. …”

“…Greater changes in neural activity arise from the switches between attractor states, which provide slow timescales for activity variation, that can span orders of magnitude [102]. Finally, attractor models can explain the observed reduction in trial-to-trial variability of neural activity upon stimulus presentation [103] when an external stimulus forces the activity into a particular state, following a period of itinerancy between many states during spontaneous activity [45,104–106]. …”

Dynamic competition between large-scale functional networks differentiates fear conditioning and extinction in humans

“…The evidence of dynamic oscillations between learning-related neural networks lends support to the view that the human brain forms a meta-stable system, in which transient networks compete with each other (Shanahan, 2010;Deco & Jirsa, 2012;Mazzucato et al, 2015). The dynamic view of brain connectivity aligns with the proposal that competition is the underlying brain mechanism by which neural resources are allocated to different learning systems without prior knowledge about the nature of the learning problem (Fanselow, 2010).…”

A gustocentric perspective to understanding primary sensory cortices

“…Recent data confirmed this suggestion in both GC [31] and lateral intraparietal area (LIP) [32]. Finally, recent work unveiled that metastability is not limited to evoked activity, but can also be observed during spontaneously ongoing activity [28] (Figure 1d). Populations of neurons in GC undergo sudden jumps between states even in the absence of any overt stimulation.…”

“…Use of the Hidden Markov Model (HMM) to extract specific patterns of ensemble activity revealed that upon gustatory stimulation populations of neurons in GC go through different states of partially coordinated activity [24-28]. Each state can last from few to hundreds of milliseconds and suddenly end, leading the network to a rapid transition into another state.…”